Deep learning for detecting and locating myocardial infarction by electrocardiogram: A literature review
P Xiong, SMY Lee, G Chan - Frontiers in cardiovascular medicine, 2022 - frontiersin.org
Myocardial infarction is a common cardiovascular disorder caused by prolonged ischemia,
and early diagnosis of myocardial infarction (MI) is critical for lifesaving. ECG is a simple and …
and early diagnosis of myocardial infarction (MI) is critical for lifesaving. ECG is a simple and …
A systematic review and Meta-data analysis on the applications of Deep Learning in Electrocardiogram
The success of deep learning over the traditional machine learning techniques in handling
artificial intelligence application tasks such as image processing, computer vision, object …
artificial intelligence application tasks such as image processing, computer vision, object …
[HTML][HTML] Robust detection of atrial fibrillation from short-term electrocardiogram using convolutional neural networks
The most prevalent arrhythmia observed in clinical practice is atrial fibrillation (AF). AF is
associated with an irregular heartbeat pattern and a lack of a distinct P-waves signal. A low …
associated with an irregular heartbeat pattern and a lack of a distinct P-waves signal. A low …
[HTML][HTML] Electrocardiogram signal classification for automated delineation using bidirectional long short-term memory
Abstract Analysis of electrocardiogram (ECG) signals is challenging due to the complexity of
their signal morphology. Any irregularity in a cardiac rhythm can change the ECG waveform …
their signal morphology. Any irregularity in a cardiac rhythm can change the ECG waveform …
Multilevel hybrid accurate handcrafted model for myocardial infarction classification using ECG signals
Myocardial infarction (MI) is detected using electrocardiography (ECG) signals. Machine
learning (ML) models have been used for automated MI detection on ECG signals. Deep …
learning (ML) models have been used for automated MI detection on ECG signals. Deep …
Beat-to-beat electrocardiogram waveform classification based on a stacked convolutional and bidirectional long short-term memory
Delineating the electrocardiogram (ECG) waveform is an important step with high
significance in cardiology diagnosis. It refers to extract the ECG morphology in start, peak …
significance in cardiology diagnosis. It refers to extract the ECG morphology in start, peak …
[HTML][HTML] Congestive heart failure waveform classification based on short time-step analysis with recurrent network
A Darmawahyuni, S Nurmaini, M Yuwandini… - Informatics in Medicine …, 2020 - Elsevier
Congestive heart failure (CHF) is characterized by the heart's inability to pump blood
adequately throughout the body without increased intracardiac pressure. Diverse …
adequately throughout the body without increased intracardiac pressure. Diverse …
Diagnosis myocardial infarction based on stacking ensemble of convolutional neural network
Artificial Intelligence (AI) technologies are vital in identifying patients at risk of serious illness
by providing an early hazards risk. Myocardial infarction (MI) is a silent disease that has …
by providing an early hazards risk. Myocardial infarction (MI) is a silent disease that has …
Feature Extraction for Improvement Text Classification of Spam YouTube Video Comment using Deep Learning
The proposed algorithms are Bidirectional Long Short Term Memory (BiLSTM) and
Conditional Random Fields (CRF) with Data Augmentation Technique (DAT). DAT …
Conditional Random Fields (CRF) with Data Augmentation Technique (DAT). DAT …
[HTML][HTML] Deep learning-based approaches for myocardial infarction detection: A comprehensive review recent advances and emerging challenges
E Radwa, H Ridha, B Faycal - Medicine in Novel Technology and Devices, 2024 - Elsevier
Myocardial infarction (MI) is a severe heart disease requiring immediate and accurate
detection for effective treatment. Deep learning (DL) algorithms have recently shown …
detection for effective treatment. Deep learning (DL) algorithms have recently shown …